Why the Digital Humanities Haven’t Embraced Data Feminism

When Data Feminism came out in 2020, it felt like a perfect match for Digital Humanities. Here was a framework that took data seriously as a site of power, bias, and politics: exactly the kind of concerns some DH scholars have been circling for years. And yet, a few years on, I can’t say the field has really embraced it.

Yes, there was some initial excitement (check out our 2023 blog post Data Feminism as a Challenge for Digital Humanities?). People cited the book, assigned it in courses, maybe even referenced its principles in talks or project proposals. But that momentum didn’t quite stick. Instead, the buzz around Data Feminism died down and conversations seem to have drifted toward other adjacent buzzwords like the “gender data gap”. This is important, too, but much narrower (maybe more concrete?). Ironically, many of these discussions could easily fall under the umbrella of Data Feminism itself. So what happened?

At the same time, DH is increasingly shaped by machine learning tools and data-driven methods. That shift makes the need for a robust ethical framework more urgent than ever. Traditional source criticism isn’t enough anymore. We also need to question how data is created, processed, and interpreted. In other words, we need a kind of digital source criticism. Data Feminism offers exactly that. But it hasn’t become a go-to toolkit in DH practice. Why not?

I suspect there are two main reasons.

1. People don’t think Data Feminism applies to them

One persistent misconception is that Data Feminism is only about gender, or only relevant if your project explicitly deals with women or feminist topics. That’s an easy way to dismiss it as “not my area.”

But that’s not what Data Feminism is about. At its core, it’s concerned with power: who gets represented in data, who gets excluded, and how systems of inequality shape what we can know. Those questions apply to every DH project, whether we acknowledge it or not. All data work is political. Even when a project seems neutral, it still makes choices about sources, categories, methods that have potentially wide-ranging or long-lasting consequences. Data Feminism gives us tools to think through those choices more critically.

The problem is that many DH practitioners don’t see themselves in that description. If you don’t think Data Feminism is “for you,” you’re unlikely to engage with it in the first place.

2. There’s an implementation gap

Even for those who are interested, there’s another hurdle: how do you actually do Data Feminism in a DH project?

The principles in Data Feminism are clear and memorable, but they’re also broad. Translating them into concrete research practices takes time and effort that many people working on DH projects simply don’t have. Engaging with feminist theory can feel like a steep learning curve, especially if your background or training did not cover them. And even if you’ve read the book, applying its ideas in practice often requires revisiting and reinterpreting them in context. In short, there’s a gap between understanding the principles and implementing them.

Where does that leave us?

If DH is serious about ethics (and it increasingly needs to be) then we can’t afford to let Data Feminism remain a nice idea that never quite makes it into practice. What’s needed now is not another theoretical discussion (although I don’t mean to downplay the value of theoretical discussions, that’s usually a ploy to sideline certain perspectives, see ‘hack vs yack’ discourse), but a usable toolkit: ways of translating Data Feminist principles into everyday research decisions. How do you document your data creation process? How do you account for absences in your sources? How do you make power visible in your visualizations?

Lowering the barrier to entry is key. That might mean more examples, more case studies, or simply more explicit conversations about how these ideas fit into existing DH workflows. Because the potential is clearly there. Data Feminism doesn’t just critique, it offers a way to do better data work. And as DH continues to expand its use of machine learning and large-scale data, that kind of framework isn’t optional anymore.

Conveniently, we wrote a paper that suggests how to implement the Data Feminism principles in DH, if you want to read more: Lang, Sarah, and Elena Suárez Cronauer. “Beyond Data Feminism. Towards Ethical Data Work in the (Digital) Humanities.” Zeitschrift für digitale Geisteswissenschaften (2026). https://zfdg.de/wp_2026

That’s it for now.
Thanks for all the fish!
The Ninja